SoroushVahidi/scidocs huggingface dataset:
Collaborative data mining for clinical trial analytics
(2015)

This paper proposes a collaborative data mining technique to provide multi-level analysis from clinical trials data. Clinical trials for clinical research and drug development generate large amount of data. Due to dispersed nature of clinical trial data, it remains a challenge to harness this data for analytics. In this paper, we propose a novel method using master data management (MDM) for analyzing clinical trial data, scattered across multiple databases, through collaborative data mining. Our aim is to validate findings by collaboratively utilizing multiple data mining techniques such as classification, clustering, and association rule mining. We complement our results with the help of interactive visualizations. The paper also demonstrates use of data stratification for identifying disparities between various subgroups of clinical trial participants. Overall, our approach aims at extracting useful knowledge from clinical trial data in order to improve design of clinical trials by gaining confidence in the outcomes using multi-level analysis. We provide experimental results in drug abuse clinical trial data.
Human-Like Rewards to Train a Reinforcement Learning Controller for Planar Arm Movement
(2016)

High-level spinal cord injury (SCI) in humans causes paralysis below the neck. Functional electrical stimulation (FES) technology applies electrical current to nerves and muscles to restore movement, and controllers for upper extremity FES neuroprostheses calculate stimulation patterns to produce desired arm movement. However, currently available FES controllers have yet to restore natural movements. Reinforcement learning (RL) is a reward-driven control technique; it can employ user-generated rewards, and human preferences can be used in training. To test this concept with FES, we conducted simulation experiments using computer-generated “pseudo-human” rewards. Rewards with varying properties were used with an actor-critic RL controller for a planar two-degree-of-freedom biomechanical human arm model performing reaching movements. Results demonstrate that sparse, delayed pseudo-human rewards permit stable and effective RL controller learning. The frequency of reward is proportional to learning success, and human-scale sparse rewards permit greater learning than exclusively automated rewards. Diversity of training task sets did not affect learning. Long-term stability of trained controllers was observed. Using human-generated rewards to train RL controllers for upper-extremity FES systems may be useful. Our findings represent progress toward achieving human-machine teaming in control of upper-extremity FES systems for more natural arm movements based on human user preferences and RL algorithm learning capabilities.
Fixed-Point Factorized Networks
(2017)

In recent years, Deep Neural Networks (DNN) based methods have achieved remarkable performance in a wide range of tasks and have been among the most powerful and widely used techniques in computer vision. However, DNN-based methods are both computational-intensive and resource-consuming, which hinders the application of these methods on embedded systems like smart phones. To alleviate this problem, we introduce a novel Fixed-point Factorized Networks (FFN) for pretrained models to reduce the computational complexity as well as the storage requirement of networks. The resulting networks have only weights of -1, 0 and 1, which significantly eliminates the most resource-consuming multiply-accumulate operations (MACs). Extensive experiments on large-scale ImageNet classification task show the proposed FFN only requires one-thousandth of multiply operations with comparable accuracy.
The Method of 2/3 Sampled Sub-Pixel Rendering for AMOLED Display
(2016)

Due to the capability of the AMOLED (Active matrix organic light emitting diode) display can provide superior color saturation, flexible utilization, slimmer screen profile and more impressive image quality, it has received increasing demand for application in electronic devices. The organic light emitting diode (OLED) is current driven device so that the characteristics of thin field transistor (TFT) influence panel uniformity. Multiple TFT of pixel circuitry has been designed to compensate the Vth (threshold voltage) variations of array process and other driving issues. The fine metal mask (FMM) used in the OLED evaporation is an accurate mask having physical margin limitations in the high PPI (pixel per inch) AMOLED application. This paper details the utilization of a 2/3 sampled sub-pixel rendering (SPR) method to liberate more 50% layout area than the RGB stripe configuration. The proposed algorithm utilizes edge detection and one-side color diffusion to improve display performance as well as RGB stripe compatibility. A 4.65” HD (720 ×1280) AMOLED panel with a 317 PPI retina display was implemented to realize the development of our SPR technology.
Bare-Bone Particle Swarm Optimisation for Simultaneously Discretising and Selecting Features for High-Dimensional Classification
(2016)

Feature selection and discretisation have shown their effectiveness for data preprocessing especially for high-dimensional data with many irrelevant features. While feature selection selects only relevant features, feature discretisation finds a discrete representation of data that contains enough information but ignoring some minor fluctuation. These techniques are usually applied in two stages, discretisation and then selection since many feature selection methods work only on discrete features. Most commonly used discretisation methods are univariate in which each feature is discretised independently; therefore, the feature selection stage may not work efficiently since information showing feature interaction is not considered in the discretisation process. In this study, we propose a new method called PSO-DFS using bare-bone particle swarm optimisation (BBPSO) for discretisation and feature selection in a single stage. The results on ten high-dimensional datasets show that PSO-DFS obtains a substantial dimensionality reduction for all datasets. The classification performance is significantly improved or at least maintained on nine out of ten datasets by using the transformed “small” data obtained from PSO-DFS. Compared to applying the two-stage approach which uses PSO for feature selection on the discretised data, PSO-DFS achieves better performance on six datasets, and similar performance on three datasets with a much smaller number of features selected.
A novel high-gain quasi-Yagi antenna with a parabolic reflector
(2015)

The simplicity and intuitive design of traditional planar printed quasi-Yagi antennas has led to its widespread popularity for its good directivity. In this paper, a novel quasi-Yagi antenna with a single director and a concave parabolic reflector, operating in S-band, is proposed. The impedance characteristic and radiation characteristic are simulated with CST-Microwave Studio, and the antenna is fabricated and measured. The measured results indicate that the antenna which can operate at 2.28-2.63GHz can achieve an average gain of 6.5dBi within the operating frequency range, especially a highest gain of 7.5dBi at 2.5GHz. The proposed antenna can be widely used in WLAN/TD-LTE/BD1 and so on.
SRP-PHAT methods of locating simultaneous multiple talkers using a frame of microphone array data
(2010)

Two new methods for locating multiple sound sources using a single segment of data from a large-aperture microphone array are presented. Both methods employ the proven-robust steered response power using the phase transform (SRP-PHAT) as a functional. To cluster the data points into highly probable regions containing global peaks, the first method fits a Gaussian mixture model (GMM), whereas the second one sequentially finds the points with highest SRP-PHAT values that most likely represent different clusters. Then the low-cost global optimization method, stochastic region contraction (SRC), is applied to each cluster to find the global peaks. We test the two methods using real data from five simultaneous talkers in a room with high noise and reverberation. Results are presented and discussed.
Mean and Variance of the Sampling Distribution of Particle Swarm Optimizers During Stagnation
(2009)

Several theoretical analyses of the dynamics of particle swarms have been offered in the literature over the last decade. Virtually all rely on substantial simplifications, often including the assumption that the particles are deterministic. This has prevented the exact characterization of the sampling distribution of the particle swarm optimizer (PSO). In this paper we introduce a novel method that allows us to exactly determine all the characteristics of a PSO sampling distribution and explain how it changes over any number of generations, in the presence stochasticity. The only assumption we make is stagnation, i.e., we study the sampling distribution produced by particles in search for a better personal best. We apply the analysis to the PSO with inertia weight, but the analysis is also valid for the PSO with constriction and other forms of PSO.
Hybrid decision tree
(2002)

In this paper, a hybrid learning approach named HDT is proposed. HDT simulates human reasoning by using symbolic learning to do qualitative analysis and using neural learning to do subsequent quantitative analysis. It generates the trunk of a binary hybrid decision tree according to the binary information gain ratio criterion in an instance space defined by only original unordered attributes. If unordered attributes cannot further distinguish training examples falling into a leaf node whose diversity is beyond the diversity-threshold, then the node is marked as a dummy node. After all those dummy nodes are marked, a specific feedforward neural network named FANNC that is trained in an instance space defined by only original ordered attributes is exploited to accomplish the learning task. Moreover, this paper distinguishes three kinds of incremental learning tasks. Two incremental learning procedures designed for example-incremental learning with different storage requirements are provided, which enables HDT to deal gracefully with data sets where new data are frequently appended. Also a hypothesis-driven constructive induction mechanism is provided, which enables HDT to generate compact concept descriptions.
Artificial Bee Colony algorithm for optimization of truss structures
(2011)

The main goal of the structural optimization is to minimize the weight of structures while satisfying all design requirements imposed by design codes. In this paper, the Artificial Bee Colony algorithm with an adaptive penalty function approach (ABC-AP) is proposed to minimize the weight of truss structures. The ABC algorithm is swarm intelligence based optimization technique inspired by the intelligent foraging behavior of honeybees. Five truss examples with fixed-geometry and up to 200 elements were studied to verify that the ABC algorithm is an effective optimization algorithm in the creation of an optimal design for truss structures. The results of the ABC-AP compared with results of other optimization algorithms from the literature show that this algorithm is a powerful search and optimization technique for structural design. © 2010 Elsevier B.V. All rights reserved.
Kqueue - A Generic and Scalable Event Notification Facility
(2001)

ApplicationsrunningonaUNIX platformneedto benotified whensomeactivity occurson asocketor otherdescriptor, andthis is traditionallydonewith theselect()or poll() systemcalls. However, it hasbeenshown that the performanceof thesecallsdoesnotscalewell with anincreasingnumberof descriptors.Theseinterfacesarealso limited in therespecthatthey areunableto handleother potentiallyinterestingactivitiesthatanapplicationmight be interestedin, thesemight includesignals,file system changes,and AIO completions. This paperpresentsa genericevent delivery mechanism,which allows an application to selectfrom a wide rangeof event sources, andbenotifiedof activity on thesesourcesin a scalable andefficient manner . The mechanismmay be extended to coverfutureeventsourceswithoutchangingtheapplicationinterface.
Multiscale Rotation-Invariant Convolutional Neural Networks for Lung Texture Classification
(2018)

We propose a new multiscale rotation-invariant convolutional neural network (MRCNN) model for classifying various lung tissue types on high-resolution computed tomography. MRCNN employs Gabor-local binary pattern that introduces a good property in image analysis—invariance to image scales and rotations. In addition, we offer an approach to deal with the problems caused by imbalanced number of samples between different classes in most of the existing works, accomplished by changing the overlapping size between the adjacent patches. Experimental results on a public interstitial lung disease database show a superior performance of the proposed method to state of the art.
Learning force control policies for compliant manipulation
(2011)

Developing robots capable of fine manipulation skills is of major importance in order to build truly assistive robots. These robots need to be compliant in their actuation and control in order to operate safely in human environments. Manipulation tasks imply complex contact interactions with the external world, and involve reasoning about the forces and torques to be applied. Planning under contact conditions is usually impractical due to computational complexity, and a lack of precise dynamics models of the environment. We present an approach to acquiring manipulation skills on compliant robots through reinforcement learning. The initial position control policy for manipulation is initialized through kinesthetic demonstration. We augment this policy with a force/torque profile to be controlled in combination with the position trajectories. We use the Policy Improvement with Path Integrals (PI2) algorithm to learn these force/torque profiles by optimizing a cost function that measures task success. We demonstrate our approach on the Barrett WAM robot arm equipped with a 6-DOF force/torque sensor on two different manipulation tasks: opening a door with a lever door handle, and picking up a pen off the table. We show that the learnt force control policies allow successful, robust execution of the tasks.
A generalized back-door criterion
(2013)

We generalize Pearl’s back-door criterion for directed acyclic graphs (DAGs) to more general types of graphs that describe Markov equivalence classes of DAGs and/or allow for arbitrarily many hidden variables. We also give easily checkable necessary and sufficient graphical criteria for the existence of a set of variables that satisfies our generalized back-door criterion, when considering a single intervention and a single outcome variable. Moreover, if such a set exists, we provide an explicit set that fulfills the criterion. We illustrate the results in several examples. R-code is available in the R-package pcalg.
Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning
(2016)

We propose a simple algorithm to train stochastic neural networks to draw samples from given target distributions for probabilistic inference. Our method is based on iteratively adjusting the neural network parameters so that the output changes along a Stein variational gradient (Liu & Wang, 2016) that maximumly decreases the KL divergence with the target distribution. Our method works for any target distribution specified by their unnormalized density function, and can train any black-box architectures that are differentiable in terms of the parameters we want to adapt. As an application of our method, we propose an amortized MLE algorithm for training deep energy model, where a neural sampler is adaptively trained to approximate the likelihood function. Our method mimics an adversarial game between the deep energy model and the neural sampler, and obtains realisticlooking images competitive with the state-of-the-art results.
Modeling and Signal Processing of Acoustic Gunshot Recordings
(2006)

Audio recordings of gunshots can provide information about the gun location with respect to the microphone(s), the speed and trajectory of the projectile, and in some cases the type of firearm and ammunition. Recordings obtained under carefully controlled conditions can be well-modeled by geometrical acoustics. Special acoustic processing systems for real time gunshot detection and localization are used by the military and law enforcement agencies for sniper detection. Forensic analysis of audio recordings is also used to provide evidence in criminal and civil cases. This paper reviews the distinctive features and limitations of acoustic gunshot analysis using DSP techniques
Adaptive fuzzy PID temperature control system based on OPC and modbus/TCP protocol
(2010)

In this paper, an adaptive fuzzy PID controller is designed to improve the control performance of the resistance furnace, and then is applied to a remote temperature control system based on modbus/TCP industrial Ethernet via OPC communication. The adaptive PID controller is developed by the fuzzy toolbox in Matlab. The data acquisition and devices driving are realized by PCAuto, a SCADA configuration software which is widely used in industry. The data exchange between Matlab and PCAuto is realized by OPC technology. The control process and results verify that the performance of resistance furnace temperature control system is satisfactory and the performance of OPC communication is very efficient and reliable.
Spotlight SAR data focusing based on a two-step processing approach
(2001)

We present a new spotlight SAR data-focusing algorithm based on a two-step processing strategy that combines the advantages of two commonly adopted processing approaches: the efficiency of SPECAN algorithms and the precision of stripmap focusing techniques. The first step of the proposed algorithm implements a linear and space-invariant azimuth filtering that is carried out via a deramping-based technique representing a simplified version of the SPECAN approach. This operation allows us to perform a bulk azimuth raw data compression and to achieve a pixel spacing smaller than (or equal to) the expected azimuth resolution of the fully focused image. Thus, the azimuth spectral folding phenomenon, typically affecting the spotlight data, is overcome, and the space-variant characteristics of the stripmap system transfer function are preserved. Accordingly, the residual and precise focusing of the SAR data is achieved by applying a conventional stripmap processing procedure requiring a minor modification and implemented in the frequency domain. The extension of the proposed technique to the case of high bandwidth transmitted chirp signals is also discussed. Experiments carried out on real and simulated data confirm the validity of the presented approach, which is mainly focused on spaceborne systems.
Cooperative Control Strategy of Energy Storage System and Microsources for Stabilizing the Microgrid during Islanded Operation
(2010)

In this paper, the cooperative control strategy of microsources and the energy storage system (ESS) during islanded operation is presented and evaluated by a simulation and experiment. The ESS handles the frequency and the voltage as a primary control. And then, the secondary control in microgrid management system returns the current power output of the ESS into zero. The test results show that the proposed cooperative control strategy can regulate the frequency and the voltage, and the secondary control action can contribute to improve the control capability.
Modeling, Analysis and Testing of Autonomous Operation of an Inverter-Based Microgrid
(2007)

The analysis of the small-signal stability of conventional power systems is well established, but for inverter based microgrids there is a need to establish how circuit and control features give rise to particular oscillatory modes and which of these have poor damping. This paper develops the modeling and analysis of autonomous operation of inverter-based microgrids. Each sub-module is modeled in state-space form and all are combined together on a common reference frame. The model captures the detail of the control loops of the inverter but not the switching action. Some inverter modes are found at relatively high frequency and so a full dynamic model of the network (rather than an algebraic impedance model) is used. The complete model is linearized around an operating point and the resulting system matrix is used to derive the eigenvalues. The eigenvalues (termed "modes") indicate the frequency and damping of oscillatory components in the transient response. A sensitivity analysis is also presented which helps identifying the origin of each of the modes and identify possible feedback signals for design of controllers to improve the system stability. With experience it is possible to simplify the model (reduce the order) if particular modes are not of interest as is the case with synchronous machine models. Experimental results from a microgrid of three 10-kW inverters are used to verify the results obtained from the model
Making microgrids work
(2008)

Distributed energy resources including distributed generation and distributed storage are sources of energy located near local loads and can provide a variety of benefits including improved reliability if they are properly operated in the electrical distribution system. Microgrids are systems that have at least one distributed energy resource and associated loads and can form intentional islands in the electrical distribution systems. This paper gives an overview of the microgrid operation. Microgrid testing experiences from different counties was also provided.
Machine Learning under Attack: Vulnerability Exploitation and Security Measures
(2016)

Learning to discriminate between secure and hostile patterns is a crucial problem for species to survive in nature. Mimetism and camouflage are well-known examples of evolving weapons and defenses in the arms race between predators and preys. It is thus clear that all of the information acquired by our senses should not be considered necessarily secure or reliable. In machine learning and pattern recognition systems, however, we have started investigating these issues only recently. This phenomenon has been especially observed in the context of adversarial settings like malware detection and spam filtering, in which data can be purposely manipulated by humans to undermine the outcome of an automatic analysis. As current pattern recognition methods are not natively designed to deal with the intrinsic, adversarial nature of these problems, they exhibit specific vulnerabilities that an attacker may exploit either to mislead learning or to evade detection. Identifying these vulnerabilities and analyzing the impact of the corresponding attacks on learning algorithms has thus been one of the main open issues in the novel research field of adversarial machine learning, along with the design of more secure learning algorithms.
 In the first part of this talk, I introduce a general framework that encompasses and unifies previous work in the field, allowing one to systematically evaluate classifier security against different, potential attacks. As an example of application of this framework, in the second part of the talk, I discuss evasion attacks, where malicious samples are manipulated at test time to evade detection. I then show how carefully-designed poisoning attacks can mislead some learning algorithms by manipulating only a small fraction of their training data. In addition, I discuss some defense mechanisms against both attacks in the context of real-world applications, including biometric identity recognition and computer security. Finally, I briefly discuss our ongoing work on attacks against clustering algorithms, and sketch some promising future research directions.